An LSTM trained on CMIP6 wind speed and pressure data is claimed to outperform MLP and Transformer-LSTM models for simulating wind power in Germany, but the evaluation lacks metrics and uses a leakage-prone random split.
Oberth¨ ur, Hard or soft governance? the eu’s climate and energy policy framework for 2030, Politics and Governance 7 (2019) 17–27
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Climate Aware Deep Neural Networks (CADNN) for Wind Power Simulation
An LSTM trained on CMIP6 wind speed and pressure data is claimed to outperform MLP and Transformer-LSTM models for simulating wind power in Germany, but the evaluation lacks metrics and uses a leakage-prone random split.